Navigation using self-describing fiducials

ABSTRACT

In one embodiment, a self-describing fiducial includes a communication element that optically communicates navigation-aiding information. The navigation-aiding information may include a position of the self-describing fiducial with respect to one or more coordinate systems and the communication element communicates the navigation-aiding information to one or more navigating objects in the vicinity of the self-describing fiducial. In another embodiment, the communication element is further configured to communicate supplementary information describing a spatial relationship between the self-describing fiducial and the surrounding environment.

RELATED APPLICATIONS

The present application is a continuation-in-part of and claims priorityto U.S. application Ser. No. 15/588,661, filed May 7, 2017, titled“Navigation Using Self-Describing Fiducials” which claims priority under35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/333,219, filedMay 7, 2016, titled “Navigation Using Self-Describing Fiducials.” Theseapplications are hereby incorporated by reference in their entirety.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments of theprinciples described herein and are a part of the specification. Theillustrated embodiments are merely examples and do not limit the scopeof the claims.

FIG. 1 is a diagram of an illustrative robust and secure autonomousnavigation system utilizing self-describing fiducials, according to oneembodiment of principles described herein.

FIGS. 2A and 2B are perspective views illustrating elements within arobust and secure autonomous navigation system utilizing self-describingfiducials, according to embodiments of principles described herein.

FIGS. 3A-3D show various self-describing fiducials and systems,according to one embodiment of principles described herein.

FIG. 4 is a flowchart of an illustrative method for robust and secureautonomous navigation utilizing self-describing fiducials, according toone embodiment of principles described herein.

FIG. 5 is a diagram of an illustrative method for installing aself-describing fiducial for a robust and secure autonomous navigationsystem, according to embodiments of principles described herein.

FIGS. 6A-6B describe illustrative systems and methods for deploying andusing self-describing fiducials in a GPS denied area, according to oneembodiment of principles described herein.

FIG. 7 shows several examples of self-describing fiducials that includeadditional information, according to one embodiment of principlesdescribed herein.

FIG. 8 shows several examples of self-describing fiducials that includeadditional information, according to one embodiment of principlesdescribed herein.

FIG. 9 shows several examples of self-describing fiducials that includeadditional information, according to one embodiment of principlesdescribed herein.

FIG. 10 shows one example of a self-describing fiducial that includesadditional information, according to one embodiment of principlesdescribed herein.

FIG. 11 shows one example of a self-describing fiducial that includesadditional information, according to one embodiment of principlesdescribed herein.

FIG. 12 shows one example of a self-describing fiducial that includesadditional information, according to one embodiment of principlesdescribed herein.

FIG. 13 shows one example of a self-describing fiducial that includesadditional information, according to one embodiment of principlesdescribed herein.

FIG. 14 shows a navigating object passing through a flow field in anurban landscape, according to one example of principles describedherein.

FIG. 15 shows navigating objects passing through a flow field in anurban landscape, according to one example of principles describedherein.

FIGS. 16-20 show various examples of self-describing fiducials thatinclude additional information, according to embodiments of principlesdescribed herein.

FIGS. 21-23 are flowcharts that describe various methods for navigationusing self-describing fiducials, according to embodiments of principlesdescribed herein.

Throughout the drawings, identical reference numbers designate similar,but not necessarily identical, elements

DESCRIPTION OF THE INVENTION

Navigation can be understood as the process of estimating an object'sposition, velocity, and attitude (PVA), along with the associateduncertainty of the same quantities. The term “guidance” can be used todescribe the control of the state (or path) of an object using theinformation obtained from the navigation process. One method fornavigation is to blend measurements from two independent, butcomplementary sources by using mathematical algorithms, such as a Kalmanfilter.

An inertial measurement unit (IMU) can provide measurements of specificforce (i.e. acceleration) and angular rate. Given an initial estimate,PVA can be propagated forward in time using measurements from an IMU.IMU measurements can be relatively accurate for the short term, but thePVA estimates can drift significantly over time due to inaccuracies inthe several IMU sensors such as bias and noise.

In contrast, GPS-based position measurements can be relativelyinaccurate over the short term, but are very stable in the long term,and can therefore be used to bound the long-term drift associated withthe IMU measurements. Furthermore, through appropriate dynamics modelinginside a Kalman filter, several error sources in the IMU measurementscan be estimated. These typically include bias, scale factor, andmisalignment errors. Thus, by observing stable measurements over time,the IMU becomes calibrated and more capable of bridging periods withlimited GPS availability. However, for prolonged GPS-denied orGPS-limited periods, the PVA drift can become problematic for successfulguidance of the object.

Due to the relatively inaccurate clocks included in consumer-grade GPSreceivers, four or more satellites can be required to determine threecomponents of position and a receiver clock bias. The accuracy of GPSmeasurements is dependent on both the variance of the range measurementto the satellites in view and the geometric arrangement of thesatellites. For example, several satellites clustered in the sameportion of the sky all have the same range variance, but since theviewing geometry is poor, this will result in an inaccurate positionmeasurement. This is termed geometric dilution of precision (GDOP).Conversely, several satellites that are more uniformly distributedacross the sky results in low dilution of precision, and a more accurateposition measurement.

There are numerous scenarios where access to GPS measurements is eitherlimited or denied, or where the accuracy of the measurement issignificantly degraded. One common scenario is natural or urban canyons,where the navigating object resides deep in the canyon and has a limitedview of the sky. In this case, the GDOP is high, the number of visiblesatellites is low, and radio wave reflections cause errors indetermining the range to the satellites. These error sourcessignificantly degrade the accuracy of GPS measurements and can causeKalman filter divergence and subsequent inability to estimate PVAresulting in inadequate guidance performance. Similar Kalman filterdivergence and failure can also occur because of intentional degradationof GPS signals through the use of GPS jammers and spoofers.

Digital cameras can be a sensor for navigation in the presence oflimited or denied GPS signals. A common approach for utilization ofimagery data provided by these onboard cameras is referred to in theresearch as Simultaneous Localization and Mapping, or SLAM. In the SLAMapproach, the state vector of the Kalman filter is augmented with thelocation of everyday features observed in the surrounding environment,and the filter attempts to compute the navigating object's PVA(localization) as well as the position of the observed features(mapping). While this approach has been demonstrated to be successful insome cases, the method suffers from significant map drift/erroraccumulation, is sensitive to incorrect correlation of features overtime, and is computationally intensive due to growth of the Kalmanfilter state vector with each observed feature. This results in costlyand power-hungry electronics and a probability of failure that isunacceptable for navigation and guidance of objects such as drones ordriverless cars.

The challenge of correlating features over time can be partiallyovercome by distributing high-contrast artificial features, orfiducials, along the path of the navigating object. However, the problemof simultaneously mapping the fiducials and using them as navigationaids in a reliable fashion persists.

The elements, systems and principles described below describe guidanceand navigation that is both low-cost and robust by developingself-describing fiducials which broadcast their position or identity,and a corresponding vision system on the navigating object that findsthe fiducial in an image frame, extracts said information, and utilizesthis information combined with the observed line-of-sight to thefiducial to perform navigation and guidance.

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present systems and methods. It will be apparent,however, to one skilled in the art that the present apparatus, systemsand methods may be practiced without these specific details. Referencein the specification to “an embodiment”, “an example” or similarlanguage means that a particular feature, structure, or characteristicdescribed in connection with the embodiment or example is included in atleast that one embodiment, but not necessarily in other embodiments. Thevarious instances of the phrase “in one embodiment” or similar phrasesin various places in the specification are not necessarily all referringto the same embodiment.

Robust navigation can be understood as a process wherein unbiased PVAestimates are maintained throughout the trajectory of the navigatingobject, independent of the availability of GPS signals. Aself-describing fiducial, as described herein, is any feature thatcommunicates information to a navigating object that allows thenavigating object to determine its position, velocity, or orientationwith respect to the fiducial. For example, a self-describing fiducialmay communicate its identity and/or location to the navigating object.In some embodiments, the self-describing fiducial may be a high-contrastfeature that communicates information including fiducial identification,location (with associated covariance) with respect to a coordinatesystem as well as ancillary information such as traffic volume levels,flight corridor heights and lateral location, speed restrictions,weather information, no fly zones, traffic routing information, trafficpatterns and pattern changes, an all-stop signal, etc.

Secure navigation systems can include the ability to encrypt theinformation transmitted by the self-describing fiducial, such thatutilization of the system is limited to authorized objects only. In someembodiments, an autonomous navigation system can have the ability toacquire the fiducial, decrypt and decode the information beingtransmitted, and utilize the observed line-of-sight to the fiducialalong with the decrypted information to autonomously estimate the PVAand the corresponding uncertainty.

FIG. 1 is a diagram of an illustrative example of several embodiments ofnavigating objects (122, 124, 126, 128) making use of a robust andsecure autonomous navigation system utilizing self-describing fiducials(102, 104, 106, 108, 110, 112, 114, 116, 118, 120). In this diagram,several navigating objects are presented including drones (122, 124), acar (126), and a person (128). Several types of self-describingfiducials are also illustrated, including a beacon on top of a lamppost(106, 108, 110, 120), on top of a traffic light post (112), on abillboard, as part of the billboard display, and/or embedded in thetraffic light (142) itself. Self-describing fiducials in the form of abarcode may also be used on the roadway (140), on the sides of buildings(130, 135) being used for navigation through the urban canyon, or ontops of buildings (102) to guide a drone to a landing location or droneservice station. The drone service station may comprise a charging pad,maintenance/repair area, or a dedicated delivery drop-off location.

An illustrative embodiment of multiple drones navigating in an urbancanyon is discussed, with descriptions of alternative embodiments whereapplicable. In FIG. 1, two drones (122, 124) are passing through anurban canyon (145) where reliable GPS signals are not available.Guidance algorithms control the trajectory of the drone (122) such thatit follows a desired path through the urban canyon (145). Throughout thetrajectory, the drone (122) should avoid collisions with surroundingstructures (130, 135) and other drones (124), meanwhile contending withdisturbances forces such as those from natural winds, turbulence fromnearby drones and ground vehicle-traffic-induced winds. Unexpectedobstacles can also exist such as new buildings, power lines,construction equipment, etc. To achieve robust, high drone trafficvolume and at the same time avoid the many obstacles present, drones canbe benefited by unbiased PVA estimates, with minimal associateduncertainty, and the ability to share their location.

The performance of the guidance algorithms, or the ability to controlthe path of the drone, is directly related to the accuracy of the PVAestimates resulting from the navigation process. If the PVA estimate isbiased, this manifests itself as an offset from the desired path of thedrone. A PVA estimate with high uncertainty manifests itself as randomphysical deviations from the desired path. Both of these characteristicsrequire a larger flight volume around the desired path, and result inlower achievable drone density and less efficient use of publicairspace. Highly accurate and precise line-of-sight measurements tofiducials with known locations can result in a PVA estimate that isunbiased and exhibits low uncertainty.

Minimizing the bias and uncertainty of the PVA estimates results in adrone that more accurately follows the desired path, enabling higherlevels of drone traffic for a given corridor volume. As discussedherein, a robust and secure autonomous navigation system can be createdusing principles related to self-describing fiducials.

FIG. 2A is a diagram of an illustrative imaging system (200) whichresides on the drone or other navigating object and includes a camerabody (201) which contains a focal plane (204). A lens (204) containsoptics which focus light (203) from an exterior scene onto the focalplane (204). In this example, the lens (202) accepts light rays (203)through an aperture (205) and focuses the light rays onto the focalplane (204) to produce the image (206) of the passive, non-changing 2Dbarcode (210) embodiment of the self-describing fiducial. In thisexample, the self-identifying information is contained in the encodedpattern of the barcode (210). The image (206) of the barcode on thefocal plane (204) is digitized and transmitted to a computing element(208) for further processing. The barcode information along with anycorresponding or related data can be temporarily or permanently storedin data retrieval system (212).

There are a wide range of different self-describing fiducials that couldbe used. For example, the barcode could include a back light that shinesvisible wavelengths through the barcode mask, enabling autonomousacquisition of the fiducial in low-light scenarios. Another embodimentincorporates a dynamic or changing barcode wherein the barcode ismodified as desired to transmit updated position or ancillaryinformation. In another embodiment of an active barcode, to increase themagnitude of the emitted light relative to the surroundings, andmaintain non-intrusiveness, the barcode emits light in non-visiblewavelengths such as IR, NIR, or UV. Another embodiment of a barcodeemploys a static or changing 3D barcode such that that all informationin the barcode is visible from any viewing angle. The 3D barcode couldbe an appropriately painted sphere or internally illuminated sphere witha barcode mask. In another embodiment, the self-describing fiducial is abokeh code. In another embodiment, the self-describing fiducial consistsof a series of symbols, whose information is decoded using characterrecognition. In another embodiment, the 2D barcode is stretched suchthat it appears uniform when viewed from a nominal viewpoint.

FIG. 2B illustrates an alternative embodiment wherein theself-identifying fiducial (216) includes an LED emitting light (213) inthe visible wavelengths, driven by a power source, which emits lightthat is modulated to transmit the self-identifying information. The lens(202) accepts light rays (213) through an aperture (205) and focuses thelight rays onto the focal plane (204) to produce the image (214) of themodulated light emitted by the LED fiducial. The intensity of the image(204) over time is digitized and transmitted to a computing element(208) for further processing. In this embodiment, the frame rate of thefocal plane (204) can limit the amount of information that can betransmitted by the LED fiducial (216) per unit time. This is referred toas data throughput. The frame rate of the focal plane (204) can beincreased by limiting the region of interest in the focal plane to thepredicted pixel location of light emitted by the LED fiducial (216). Theprocess of limiting the region of interest can be facilitated usingcurrent PVA estimates propagated using IMU measurements. This processalso increases robustness by preventing multi-path (reflected) signals,spoofed observations, and minimizing false observations or falsepositives. Additionally, the gross motion of the image can be measuredvia optical flow, providing a prediction of where the light emitted bythe LED fiducial (216) will be in the following image. In anotherembodiment, a quad-cell, or a four-pixel focal plane may record the timevarying intensity of the observed LED fiducial at a very high rate,while a high resolution focal plane provides the observed line-of-sight.In another embodiment, to increase the magnitude of the emitted lightrelative to the surroundings, the LED may emit light in non-visiblewavelengths such as IR, NIR, or UV. In another example, the wavelengthof light of a light emitting self-describing fiducial (216) is selectedto maximize observability and signal to noise ratio in expectedenvironmental conditions, such as dust, fog, snow and rain.Alternatively, the self-describing information can be distributed overdifferent wavelengths of light, thus multiplying the data throughput bythe number of wavelengths utilized. In another embodiment, the fiducialmay include more than one LED, both to increase the data throughput andto provide additional information. In another embodiment, polarizationof the emitted light is used to preclude multi-path (reflected)observations. In another embodiment, the LED beacon broadcastsinformation at a very narrow wavelength, while the receiving camera hasa band-pass filter which limits the incoming spectrum to the wavelengthof interest, and rejects light from other wavelengths. The robustness ofthe LED-based fiducial system to incorrect or incomplete signals, mayalso be increased by using signal processing techniques such aschecksums or similar algorithms.

The LED fiducial is only one example of a light emitting fiducial. Avariety of other light emitters may be used, including lasers, laserdiodes, broad band light emitters, or modulation of existing emitterssuch as street lights, billboards, projectors, outdoor electronicdisplays, lighted signs, etc. In one example, a laser system situated ina key location projects QR code self-describing fiducials onto facingbuildings, using laser rangefinding information to estimate the positionand position covariance of the fiducial, which information is embeddedin the QR code for communication to navigating objects in the vicinity.In another example, large buildings form oversized fiducials bypreferentially lighting or modulating light in rooms which are visiblefrom the outside. In one embodiment, a triad of lasers at orthogonalangles to each other and of known different wavelengths, emanate from asingle point and communicate self-describing fiducial informationthrough modulation or similar means, from which fiducial the attitude ofthe navigating object can be directly measured in a single observationas well as the fiducial's position (the position of the origin, for usein a line-of-sight measurement). The frequency of the lasers may beselected so that the lasers are visible as they pass through thesurrounding air. In another embodiment, an active, self-describingfiducial transmits additional information such as an indication that thenavigating object is off a prescribed path, communicating motioncorrection commands much like a light bar on an aircraft carrier.

FIGS. 3A-3D shows a variety of different self-describing fiducials. FIG.3A shows a globe (300) that has a barcode printed on it. In someexamples, the globe fiducial may be internally illuminated for bettercontrast viewing of the bar code. In another example a glowing ball ofknown diameter with internal lighting or laser illumination forms aself-describing fiducial. In another embodiment, modulated sets ofbrake/tail/headlights on ground vehicles or navigation lights on airvehicles form self-describing fiducials which enable vehicles todistribute navigation information and calculations, with baseline lightemitter separation distance included as part of the communication packetwith which observers can extract additional range and attitudeinformation from an observation. Further, the concepts and principlesdescribed above may be combined or used with additional elements toachieve the desired navigation outcomes.

In some examples, the navigating drones may be able to make rangeobservations based on the apparent size of the fiducial or the featureseparation of elements of the fiducial. For example, the fiducial mayhave a known shape and/or size. As shown in FIG. 3B, a circular fiducial(302) may have a known diameter and when placed on a flat surface andwhen observed from a wide range of angles it appears as an oval with amajor axis that corresponds to the diameter of the circle. Thus, theapparent size of the major axis of the oval allows for an estimate ofthe range from the fiducial to the drone to be made. The ratio of themajor axis of the oval to the minor axis of the oval can provide anestimate of the angle of observation to be made.

FIG. 3C shows a barcode (304) that is wrapped around a cylinder so thatit can be observed from 360 degree angles. The data on the barcode (304)may be repeated so that the information can be retrieved from thebarcode at any angle.

Other examples of fiducials that can allow for range or angle estimationmay include QR codes with discernible features that have a predeterminedsize or spacing. In one embodiment, there may be multiple fiducials thathave a known or estimated relationship to each other (a fiducialconstellation or compound fiducial). A simple example of a constellationmay be three points in a plane and one point out of the plane. This willcreate coordinate system that provides at least a local locationreference, and in many cases an attitude reference. For example, a firstfiducial may be located on an upper corner of a rectangular building andan additional fiducials located on one of the edges that join to formthe corner. This and other compound fiducial configurations may providea number of benefits such as the ability of the drone, after locatingone of the fiducials in the constellation to predict where otherfiducials in the constellation will be located. This allows the drone towindow down its camera and/or limit its analysis of the camera data tospecific locations. This may save computational time, conserveprocessing power for other tasks, and may reduce the energy required bythe processor.

Additionally, if the spatial relationship between the fiducials is knownwith a high degree of certainty, a location estimate of one of thefiducials can be translated to other fiducials in the constellation. Itmay also reduce the amount of data used to describe or communicate thefiducial information . . . the location of the origin and attitude canbe transmitted as the complete description of the location of all of thefiducials in the constellation. Under ideal circumstances the locationof each of the fiducials in the constellation would be known to a highlevel of accuracy and the relationship information between the fiducialsin the constellation would be redundant. However, in real worldconditions where the fiducials are placed in a GPS denied environment,the absolute location of the fiducials may be more difficult to obtainthan the local relationships between the fiducials. Consequently,observation of the fiducial constellation can provide additionallocation information in some circumstances. For example, by knowing therelationship between the fiducials in the constellation, additionalrange and orientation information can be obtained from observations. Inone example, the fiducials can be installed and then communicate betweenthemselves, calculate a range between each other, and then initializethemselves into a local coordinate system. In some circumstances, thisinformation may be sufficient for limited navigation of the drone orother navigating object because the drone may not need absolutepositioning once it gets to a flight corridor. It can navigate along theflight corridor without absolute position if it knows its position andthe position of the flight corridor relative to the local coordinatesystem created by the fiducial constellation.

FIG. 3D shows a constellation of self-describing fiducials (306, 308,310). These fiducials can communicate between each other using acommunication network (312). The fiducials may communicate any relevantinformation, include their absolute or relative positions, informationrelating to drones or other navigating objects, transit coordinates,etc. These fiducials may include any of the features or elementsdescribed above, separately or in combination. For example, they mayinclude circular QR codes with known sizes to allow for range and angleapproximations to be made by observing objects (314). They may includeencoded information in the QR code that communicates the location of thefiducial and/or an identity of the fiducial that allow for additionalinformation to be accessed or retrieved. The fiducials may be backilluminated and/or may include modulating intensity or frequency oflight to communicate information between other fiducials or tonavigating objects. The fiducials may also include wirelesscommunication capabilities that allow for one way or two waycommunication with other fiducials, navigating objects or otherelements. For example, a navigating object (314) may be able todetermine approximate distance from an active fiducial (310) using anumber of techniques, including time of flight of an RF transmission,power of a transmitted signal, or other techniques. This information canbe combined with line of sight observations to assist the navigatingobject.

FIG. 4 is a flowchart showing an illustrative example of a system andmethod (400) for robust, secure autonomous navigation system utilizingself-describing fiducials. According to one illustrative embodiment thenavigation system is initialized using IMU measurements from zerovelocity conditions and GPS position measurements. While available, GPSposition measurements are processed, resulting in PVA estimates andassociated covariance estimates.

Prior to entering a GPS-denied environment, the image (403) produced bythe focal plane (step 402) is acquired (step 404) and scanned for acandidate barcode or other fiducials. This may include searching awindowed image frame (405). Once the barcode fiducial is acquired (step406), the barcode pattern (409) is decoded to extract a dataset (step408). The dataset may be an encrypted SDI (411) or may directly producethe desired information. The decryption (step 410) may be performedusing an onboard key and to extract the self-describing informationcontained in the barcode fiducial. Additionally or alternatively thedecryption or decoding process may access a database of information thatis indexed according to the information communicated by theself-describing fiducials.

Once the self-describing information (413) is obtained, additionalinformation can be obtained from a database lookup (step 412). Theposition and other information obtained (415) can then used to predictthe line-of-sight of the fiducial in the next image frame (step 422),given the current PVA estimates.

The position of the fiducial (415) and the fiducial location on theimage plate (407) are used in a line of sight (LOS) processing module(414) to calculate LOS data (417) as an input into the navigationKalmann filter (420). The LOS data (417), GPS data (419) from the GPSunit (416), if available; and inertial data (421) from the IMU (418) canbe combined in the navigation Kalmann filter to produce a PVA estimate(423) that allows the drone or other navigating object to understand itscurrent position, velocity and attitude, thereby providing theinformation to navigate. Additionally, the Kalmann Filter may output afiducial prediction (422) that predicts where the current fiducial isexpected to be and/or where the next fiducial(s) are expected to be. Ifthe location of the fiducial is uncertain or has an estimated errorbeyond a limit, revised data (425) produced from the fiducial predictionmay be incorporated into the database (412). This will improve thedatabase information/lookup progressively as more observations of thefiducials are made. The fiducial prediction module (422) may alsoproduce a prediction of the fiducial location (427) that can be used toacquire the self-describing fiducial by predicting a window within theimage frame that the fiducial is expected to be in. Ideally this allowsfor faster processing of the image because only a portion of the imageneeds to be searched to locate the fiducial.

Additionally or alternatively, a grid system is utilized to minimizedata throughput requirements, and to gradually transmit higher precisionposition information of the fiducial. In the grid system, a limitednumber of bytes are transmitted which specify the grid square that thevehicle is currently located in. Subsequent bytes specify the locationof the vehicle inside the grid, with adjustable precision.Alternatively, data could be transmitted from most significant bit toleast significant bit, such that the precision of the fiducial increaseswith the duration of lock-time on the fiducial.

In some examples, the measured line-of-sight of the barcode fiducial canbe obtained directly from the digitized image. The difference betweenthe predicted line-of-sight and the measured line-of-sight forms theresidual. A nonzero residual is indicative of PVA estimation errors andis used inside the Kalman filter to provide first-order corrections tothe PVA estimates. The current and possibly past PVA estimates provide aprediction of where in the current image the previously-acquiredfiducial is likely to appear, which enables a search window of limitedsize and higher frame rate.

In addition to the embodiments described above, the principles describedabove may be implemented in a variety of ways. For example, theself-describing fiducials may serve as a complementary navigationsystem, in combination with available GPS signals, and/or serve as astandalone navigation aiding system which could replace the informationgained from GPS, or serve as an independent terrestrial alternative,providing increased robustness and an independent indication of GPSsignal failure.

Additionally, the self-describing information may be decoded anddecrypted to obtain ancillary information such as traffic volume levels,flight corridor heights and lateral location, speed restrictions,weather information, no fly zones, traffic patterns and pattern changes,an all-stop signal, etc. In another embodiment, the corrections producedby the Kalman filter provide corrections to the PVA estimates that areaccurate to second-order, accounting for the nonlinearities present inline-of-sight observations. In another embodiment, alternative filteringtechniques such as a least-squares filter, an unscented Kalman filter,an information Kalman filter, or particle filter are employed to providecorrections to the PVA estimates. In another embodiment, navigatingobjects maneuver such that PVA are optimally estimated given the spatialdistribution of the self-describing fiducials in the vicinity. Inanother embodiment, a wearable self-describing fiducial is used forrelative navigation of a drone which serves as a personal fan, light orobservation post. In another embodiment, a wearable self-describingfiducial is used to enable people routing at large events, with overheaddrone ushers shining color coded lights, forming person orgroup-specific paths on the ground which direct people to their entrypoints, exit points, restrooms or parking spots.

FIG. 5 illustrates one illustrative installation process ofself-describing fiducials. In a representative embodiment (500), thefiducials (502, 512) can be installed in a variety of ways, including bya manned crew (514) on elevated structures (510) inside the urban canyon(515) where GPS signals are typically unreliable. Localization of thefiducials (502, 512) can be performed by long-term GPS observations,long enough to remove the effects of limited GPS satellite visibility,as well as ionospheric and tropospheric noise terms. In anotherembodiment, localization of the installed fiducials is performed by anobserver drone above the urban canyon with stable GPS signals. Byobserving the line-of-sight to the newly-installed fiducial (andpreviously-installed self-describing fiducials within the field of view)during translation of the observer drone(s) (504, 506), the location ofthe newly-installed fiducial (512) can be autonomously calculated usingwell-known methods such as bundle adjustment or the coplanarityconstraint equations. In addition, observing previously-installedself-describing fiducials (502) during the localization process of newfiducials (512) further improves localization accuracy. Additionally theamount of translation performed by the observer drone can be moreaccurately measured using carrier phase GPS signal processing. Inanother embodiment, new self-describing fiducials can be installed by adrone that utilizes previously-installed and previously-localizedself-describing fiducials for navigation and accurate guidance.

Additionally, the spatial distribution of the self-describing fiducialscan be selected such that the navigating objects achieve a desirednavigation accuracy with respect to one or more coordinate systems.Given a set of requirements for the navigation system (such as accuracy,navigating object velocity, number of beacons visible at all pointsalong a predetermined path to be traveled, system installation andoperational costs, etc), optimization algorithms such as Monte Carlo,gradient descent, linear programming, or convex optimization can be usedto determine an optimal physical distribution of beacons in theneighborhood of the path. If desired, the beacons can also be placedwith sufficient physical distribution (both horizontal and vertical)such that the position and attitude of the navigating object can beuniquely calculated from a single observation.

Additionally or alternatively, given a set of beacons physicallydistributed in the neighborhood of a flight corridor, optimizationalgorithms such as Monte Carlo, gradient descent, linear programming, orconvex optimization can be used to determine a flight path thatoptimizes a set of requirements such as state observability, number ofbeacons instantaneously within the FOV, covariance of PVT estimates orIMU calibrations. Further, a drone or other navigating object maytranslate along a nominal path while simultaneously perturbing itsattitude independent of the path to optimize navigation aiding fromobservations of the self-describing fiducials.

In one example, drones that may need to re-calibrate sensor parametersin flight (including IMU scale factor, bias, misalignments, cameraparameters such as distortion, focal length, magnetometer bias, etc.)can enter a navigation estimate maintenance corridor (either above,below or to the side of normal traffic corridors) where it has morefreedom to perform optimal maneuvers for sensor parameter estimation,such as circles, figure eights, or Lissajou curves.

In one embodiment, the system of self-describing fiducials is made up ofa fleet of drones or other mobile equipment. For example, along aspecific route there may be 10 specialized drones that fly intostrategic positions and broadcast their position using one or moreself-describing fiducials. The specialized drones may initially fly at arelatively high altitude to fix their position using available GPSinformation and register the locations of visual landmarks below them.They can then descend into the urban canyon or GPS denied environmentand supplement their IMU data with visual landmarks and any GPSinformation that may be available. In some embodiments, the drones thatare descending into the GPS denied environment may use another portionof the drone fleet above them as fiducials to more accurately determinetheir position as they descend. Once the lower fiducial drones are in adesired location/distribution, they can secure themselves to surfacesand act as passive or active fiducials to the drone or other navigatingobject traffic in the GPS denied area.

Additionally or alternatively, drones may place separate fiducials onsurfaces in the GPS denied environment. For example, a drone may gatherinformation about its location and place a printed sticker containing aself-describing fiducial at that location and then move on a differentlocation to place the next printed sticker. In other embodiments, thedrone may apply a self-describing fiducial mark using other techniquessuch as inkjet printing, spray painting or laser etching. For examplethe drone may include a reservoir of visible or IR ink and a method ofdispensing the ink onto a surface that will support the self-describingfiducial. Other techniques may include laser etching surfaces so thatthe etched and unetched surface reflect light differently at a desiredwavelength or range of wavelengths. In one example, the drone may simplycarry self-describing fiducial modules that are attached to structuresor the ground and transmit their location or other information. Forexample, the drone may have a number of self-describing fiducial dartsor rounds that can be shot at building or structures. The darts/roundsmay have an embedded LED that transmits information or communicatenavigation information in some other way. The darts/rounds may partiallypenetrate or adhere to the structures.

In one embodiment, a drone may or may not have information thatdescribes the location of a self-describing fiducial at the time thefiducial is placed or created. The fiducial may simply identify itselfin a way that allows for the desired information to be referenced from adifferent source. For example, a relief operation may be undertaken inan urban environment where GPS signals are being disrupted by buildingsand/or by active jamming equipment. A fleet of “marking” drones entersthe area and makes self-describing fiducial markings on buildingswithout having precise information about the location of the fiducials.The information encoded/transmitted by the fiducials have generic labelsthat at least identify the fiducial. A “best guess” of the fiducialposition and position covariance can be made by the “marking” drone andentered into a matrix/database. As “mule” drones enter the area carryingthe desired payloads, they optically identify the fiducials and look upthe approximate location of the fiducials from the matrix/database. Theyuse this location information to navigate and supplement their IMUsensors/calculations and any other guidance information that can bederived from GPS or visual data. However, the collectivelocation/navigation information from the set of navigating drones isalso used to fine tune the matrix/database of fiducial information.Thus, the matrix/database of fiducial locations may initially berelatively coarse, but becomes more refined over time as more dronescontribute navigation information or other sources of locationinformation are obtained. In one example, drones that enter the GPSdenied environment may navigate through the area as best they can usingSLAM along with whatever fiducial information and sensor synthesis isavailable. They go into the area with a high accuracy/precisionnavigation information and can transfer this information to the matrixfor the fiducials that they first encounter. They may lose accuracy asthey progress into the GPS denied environment and then recover accuracyas they exit the area or cross an already refined set of fiducials. Asthey discover the errors in their navigation data upon exiting the GPSdenied environment, their observations of fiducials before they exitedthe GPS denied environment can be improved and propagated into thefiducial matrix/database. Thus, the accuracy of the matrix/databaseimproves over time because of the accumulated observations.

FIGS. 6A and 6B illustrate one example of the above principles. FIG. 6Ashows a plan view of a GPS denied area (600). The GPS denied area (600)may be actively denied by jammers/spoofers and/or may include urbancanyons (610). The “marking” drone may place/identify the first severalfiducial marks (A, B, C) with an accuracy of centimeters but as itproceeds deeper into the GPS denied environment the covariance of thefiducial locations (D, E, F, G) may increase to meters or even tens ofmeters. The fiducials mark a drone path or corridor (605). Thefiducials, once placed and associated with location information, may beused for ground based drones, air-based drones, missiles, ground troops,first responders or other navigating objects.

The first “mule” drone that is delivering humanitarian supplies canprogress with confidence through the first fiducials (A, B, C) and thenrely on primarily on its IMU data as it continues deeper into the GPSdenied area. Its observations of the next several fiducials (D, E) maybe fairly accurate and are propagated to the matrix/database. However,the “mule” drone may make less accurate observations of fiducials F andG. But as it exits the GPS denied environment close to F and G, theaccuracy with which it knows its position increases dramatically. Thisinformation is propagated backward in time to increase the accuracy ofthe last several fiducial observations (F, G). Consequently, asadditional drones pass through the area, each can progress with moreconfidence in the area marked by fiducials (A, B, C, D, E, F, G) andcontribute more accurate PNT information to the matrix/database fromtheir own observations.

The example given above is only illustrative. A variety of variations,combinations, and extensions of the principles described could be used.For example, drones could be specialized to improve the distribution offiducials and rapidly increase the location accuracy of the fiducialmatrix. “Marker” drones may be adept at visually identifying desirablelocations for fiducials and accurately placing the fiducials on thesurfaces. self-describing fiducials may be placed near high contrast,pre-existing landmarks, creating an optimized coarse-fine fiducialmatrix. A “surveyor” drone with superior navigation/communicationequipment may follow the marker drone(s) and refine the locationestimates of the fiducial matrix. The “mule” drones may simply use thefiducials or may contribute location observations to thematrix/database.

In other embodiments, a single drone may perform all tasks. For example,a drone may have a marker payload and enter the GPS denied environment,place its markers, transmit (if possible) or store the best knownlocation of the self-describing fiducials and select an exit from thearea that increases its chances of obtaining relevant navigationinformation to add to the fiducial matrix/database. It then obtainsanother marker load and reenters the area to improve the locationinformation for the previously placed fiducials and to place/locateadditional fiducials. It may take a different route within andenter/exit the area from different locations to optimize the navigationinformation. The drone may then have access to enough self-describingfiducials to navigate with a payload to a specified location with arequired accuracy.

Additionally or alternatively, multiple “marker” drones may enter theGPS environment from different directions/routes and placeself-describing fiducials as they go, then cross paths or exit alongentry paths of other marker drones. This will allow the exiting dronesto make observation of fiducials placed by other drones and allow formore precise navigation during the exit. The observations of all thedrones are combined (in some examples in real time) to create a moreaccurate self-describing fiducial matrix/database. FIG. 6B shows oneexample of a self-describing fiducial matrix (615). This matrix mayinclude a variety of information, including a fiducial identifier(Fiducial ID), location information of the fiducial (X, Y, Z), an errorestimate of the location (Error Est.), a relative location (Rel. Loc.)of other fiducials or landmarks, updated information (Update Data) thatrecords data that was added/improved in the matrix, a waypoint log(Waypoint Log) that records which navigating objects have encounteredthe fiducial, and other data. This other data may be any data that hasbeen described herein or other data that could be useful to thenavigating objects or self-describing fiducial system.

As described above, a self-describing fiducial navigation system may bea low-cost, resilient backup navigation system for GPS denied situationssuch as tunnels, urban canyons, extreme weather events,GPS/GLONASS/GALILEO satellite dropouts or destruction during wartime. Inone example, a drone could serve as a self-describing fiducial in anemergency (modulating one or more LEDs of its own). This capability issynergistic with a quick response, for example to an accident on a busyroadway, since the drone would get to the scene of the accident before apatrol car could, and would assess the situation, determine medicalpersonnel needs, broadcast the precise location of accident, andre-route traffic around the accident site. In another embodiment, QRcodes or other self-describing fiducials could be placed on mile markersand other plentiful signage “real estate” along roadways. In someinstances, a pre-existing sign could be used as a self-describingfiducial. For example, mile markers are optically read by a navigatingobject and associated with their location in a database. Additionally,electronic signs that incorporate light banks or lighted TV boards couldbe used as updateable fiducials. In some examples, the electronic signscould be used as compound fiducials by illuminating only a portion ofthe sign. For example, corners of a large electronic sign could bemodulated to form a large 2D self-describing fiducial. In some instancesthe 2D self-describing fiducial may broadcast its baseline distance toallow for range and attitude estimates to be made. As discussed above,the sign fiducials may broadcast other information such as weather andwind information or the location of a recharging station or drop-offpoint.

The modulation of light (i.e. changing the intensity of the light) hasbeen discussed as one way for a self-describing fiducial to transmitinformation. However, there are a number of different parameters inaddition to the intensity of the light that could be modulated. Forexample, the frequency or phase of light or other electromagnetic signalproperty could be modulated. One example may be a self-describingfiducial that broadcasts radio frequency signals that are monitored by adrone. The drone could use the doppler effect to obtain a range rate(RF) measurement, or time of flight from radar to obtain a rangemeasurement. In another example, the navigating object emits awavelength of electromagnetic radiation, such as light in a flash lidarsystem, and self-describing fiducials with phosphorescents respond witha known signature, much like echolocation for bats, but withpurpose-built self-describing fiducials. In another embodiment, aholographic projector is used to project three dimensionalself-describing fiducials. In this case, the viewing angle determineswhat is seen, providing additional navigation information. A relatedembodiment is self-describing fiducials formed from active or reflectivebokodes, which can be less intrusive than QR codes.

Other sources of information can also be used to improve the accuracy ofthe fiducial matrix, including laser range finding to measure distancesto/between fiducials, optical matching to photo reconnaissance images ordata, manual observations by support personnel, sonar or radarobservations, or other sources.

As described above, the self-describing fiducial enables accuratenavigation and associated improved guidance GPS-degraded or deniedenvironments. This enables higher traffic density at acceptable safetylevels when GPS is not available. Supplemental information could furtherincrease the overall efficiency and safety of an object performingnavigation and guidance. This is accomplished by providing at least oneof navigation-aiding information and guidance-aiding information, asdescribed below.

As described herein, a self-describing fiducial communicates its ownposition with respect to one or more coordinate systems. A navigatingobject in the vicinity of the self-describing fiducial uses line ofsight measurements to one or more of these now known positions to aidits navigating estimation system over time. In many cases thisnavigation estimation system includes a variant of a Kalman or similarestimation filter, and the addition of self-describing fiducialobservations as an aiding source results in an improved estimate ofnavigation states such as position and attitude of the navigatingobject. Aiding sources that provide more accurate navigation aidinginformation will improve the quality of resulting navigation stateestimates more than aiding sources that provide less accurateinformation. In a similar way, aiding sources with one or moreadditional dimensions of navigation aiding information will improve thequality of resulting navigation state estimates more than aiding sourceswith fewer dimensions.

In one example, the self-describing fiducial communicates not only itsown position, but also at least one component of its own attitude withrespect to one or more coordinate systems. This compromises at least oneadditional dimension of navigation aiding information. In thisembodiment, the self-describing fiducial is also enhanced with at leastone associated attitude-describing feature that is observable bynavigating objects in the vicinity, indicating to the navigating objector objects at least one component of the self-describing fiducial's ownattitude with respect to one or more coordinate systems.

Navigating objects in the vicinity can extract a more powerfulnavigation measurement from a single observation of this enhancedself-describing fiducial due to the additional dimensions ofnavigation-aiding information. Attitude-describing features that haveobservable directionality could include a row of light emitters, avisible arrow or arrows, lines, patterns of elements, motion or apparentmotion of observable features such as running lights, orientation of apattern such as a quick response (QR) code or projection of suchfeatures onto a surface like a roadway or the wall of a building. Insome instantiations of this embodiment, the communicating element of theself-describing fiducial also comprises the attitude-describing featureor features. In other instantiations, these are separate elements. Anattitude-describing feature can point in cardinal directions of one ormore coordinate systems, such as north, up or down, or in otherinstantiations these features can point in other directions.

Observing a self-describing fiducial that also has at least oneattitude-describing feature using an imaging system captures aprojection of at least one component of the attitude-describing featureas measured at the sensor focal plane at a specific time. Thisobservation, in combination with the communicated information about atleast one component of the self-describing fiducial's own attitude,comprises the additional dimension or dimensions of information that thenavigation estimation system uses to produce more accurate estimates ofthe navigation states of the navigating object.

In some examples, the attitude-describing feature is selected tomaximize its effectiveness as an aiding source by increasing visibility,the ability of the navigating object to resolve the feature and/or thedirectionality of the feature. For example, in an urban canyon anenhanced self-describing fiducial with an attitude-describing featurethat resides in a vertical plane provides increased accuracy related tothe roll of the navigating object, which translates into improved rollcontrol, and an associated reduction in the cross-track position error.As another example, an enhanced self-describing fiducial with anattitude-describing feature that resides in a horizontal plane below thenavigating object provides improved yaw accuracy, which translates intoimproved heading control, and an associated reduction in the cross-trackposition error.

In another or in an alternative embodiment, the self-describing fiducialcommunicates not only its own position, but also at least one componentof the look vector from the self-describing fiducial to the navigatingobject with respect to one or more coordinate systems usingperspective-based information encoding. This comprises at least oneadditional dimension of navigation aiding information. In one example ofthis embodiment, the attitude-describing feature could be a pattern,barcode or QR type code wrapped or projected onto a cylinder or sphere.The navigating object can only observe a portion of the complete matrixbarcode or pattern that is visible from its particular perspective. Thebarcode is designed such that the portion of the barcode visible fromthe particular perspective communicates at least one component of thelook vector from the self-describing fiducial to the navigating objectwith respect to one or more coordinate systems. Navigating objects inthe vicinity can extract a more powerful navigation measurement from asingle observation of the self-describing fiducial due to thisadditional information.

FIG. 7 illustrates alternative embodiments (702, 704, 706) where theself-describing fiducial communicates not only its own position viainformation in the barcode (710), but also at least one component of itsown attitude with respect to one or more coordinate systems. Thiscompromises at least one additional dimension of navigation aidinginformation. In this embodiment, the self-describing fiducials (702,704, 706) are also enhanced with at least one associatedattitude-describing feature (708) that is observable by navigatingobjects in the vicinity, indicating to a navigating object or objects atleast one component of the self-describing fiducial's own attitude withrespect to one or more coordinate systems. The self-describing fiducials(702, 704, 706) could include any number of associatedattitude-describing features (708). These attitude-describing featuresmay have any of a number of different characteristics such as shape,size, or type. In the example of (702), the associatedattitude-describing feature (708) is comprised of a shape (708) locatedin the lower left hand corner, a shape (708) located in the upper lefthand corner, and a shape (708) located in the upper right hand corner.The relationship between the position of the shapes (708) couldcommunicate to navigating objects information regarding theself-describing fiducial's (702, 704, 706) attitude with respect to oneor more coordinate systems.

In one example, the self-describing fiducial (702) communicates not onlyits position using the barcode (710), but also communicates itsorientation with respect to the ground using the associatedattitude-describing feature (708). A navigating object could observe theassociated attitude-describing feature (708) and then determine its ownorientation and navigate in a desired direction. For example, theassociated attitude-describing feature (708) could communicate to anavigating object which direction is vertical, and also communicate thatthe navigating object should turn right.

In another example, a self-describing fiducial (704) could communicateits position using the barcode (710), and the associatedattitude-describing feature (708) could communicate to navigatingobjects which direction is vertical with respect to the ground. Theassociated attitude-describing feature (708) could also communicate to anavigating object that it should turn left.

In another example, a self-describing fiducial (706) could communicateits position using the barcode (710), and use the associatedattitude-describing feature (708) to communicate to a navigating objectthat it should travel in a vertical direction.

FIG. 8 shows a number of different embodiments (800, 808) that includean attitude-describing feature (802) that points in the verticaldirection. This attitude-describing feature (802) can have a variety ofdifferent configurations, including the different arrows shown. Thearrows or other attitude-describing features can be selected based onany number of criteria including high visibility for the navigatingobject and easy decoding of the intended information. As discussedabove, the barcode (804) may or may not contain information describingthe attitude-describing feature (802). For example, the barcode (804)could contain information relating to the location of theself-describing fiducial (800, 808) (e.g. latitude, longitude, altitude)and could additionally communicate that an attitude-describing feature(i.e. 802) is an arrow pointing in the upward (gravitational reference)direction. The attitude-describing feature (802) could also be furtherdescribed by the information communicated by the self-describingfiducial (800, 808). For example, the size, geometry, coloring, relativelocation with respect to the barcode (804), could reference to analready known shape library or decoding parameters/technique such as theScale Invariant Feature Transform (SIFT). Other features, such asalignment marks (806) could also be present and supplement theinformation available to the navigating object.

FIG. 9 shows two examples of a self-describing fiducial (900, 910) withmultiple attitude-describing features (902, 908). In one example, thecombination of arrows (902, 908) describe both the plane that theself-describing fiducial (900, 910) resides in and the orientation ofthe self-describing fiducial (900, 910) within the plane. In anotherexample, the arrow (902) may describe the orientation of theself-describing fiducial (900, 910) while the additional arrow orgeometry (908) could give additional information about the environmentor the path of the navigating object. For example, the darker arrows(908) could indicate that an aerial path that the navigating objectshould follow is to the right of the fiducial (900, 910).

FIG. 10 shows a self-describing fiducial (1000) that is made of LEDs(1002, 1004). In this example, the LEDs are aligned along an axis(1006). For example, there may be four LEDs (1002) with a first color,brightness, wavelength, or flashing pattern and a different LED (1004)with a different characteristic. In one embodiment, the self-describingfiducial (1000) could use the different LEDs (1004) to communicate bothinformation regarding the position of the self-describing fiducial(1000) and the orientation of the axis (1006). In one embodiment, theself-describing fiducial (1000) could use the different LED (1004) tocommunicate to a navigating object information about a path thenavigating object should follow. In a different embodiment, theself-describing fiducial (1000) could communicate information about theenvironment using the LEDs (1002, 1004). For example, when the differentLED (1004) is flashing at a certain frequency, it could indicate to anavigating object that windy conditions are ahead, and when notflashing, indicate to a navigating object that calm conditions areahead.

FIG. 11 shows a self-describing fiducial (1100) that includes a numberof LEDs (1108, 1110) arranged in a particular pattern. In this examplethe LEDs (or other object/light sources) are arranged along three axes(1102, 1104, 1106) which may be orthogonal. In one embodiment, theself-describing fiducial (1100) could be used to communicate a pathway anavigating object should travel. In another example, the self-describingfiducial (1100) could communicate information about the environmentsurrounding the self-describing fiducial (1100). For example, whencertain LEDs are flashing, it could communicate the velocity anddirection of wind in the area to a navigating object. Theself-describing fiducial (1100) could also be used to determine theorientation of the navigating object with respect to the self-describingfiducial (1100) in three directions. In another example, theself-describing fiducial (1100) could communicate information aboutaerial traffic to a navigating object so that the navigating objectcould adjust its flight pattern accordingly. In one example, when an LED(1110) along a certain axis is flashing, it could communicate to anavigating object that another object is coming towards theself-describing fiducial (1100) at a certain angle in relation to theaxes (1104, 1102, 1106).

FIG. 12 shows a self-describing fiducial (1200) that can be made of upof a geometric shape. In this example, the geometric shape is a cylinder(1206) with a matrix barcode (1204) wrapped around the perimeter andvarious alignment marks (1202) on the barcode (1204). Thisself-describing fiducial (1200) could be used in a variety of waysincluding perspective-based information encoding where theself-describing fiducial communicates an angular “look” direction thattells navigating objects from which direction they areapproaching/looking at the fiducial (1200).

FIG. 13 illustrates yet another embodiment, where a self-describingfiducial (1300) is enhanced to communicate both: 1. at least onecomponent of its own attitude and has at least one associatedattitude-describing feature (1304), and 2. at least one component of thelook vector from the self-describing fiducial (1300) to the navigatingobject using perspective-based information encoding. This comprises theadditional dimensions of information that the navigation estimationsystem uses to produce more accurate estimates of the navigational stateof the navigating object. In some instantiations of this embodiment, asingle observation of such an enhanced self-describing fiducial (1300)can provide the navigating object with a measurement containinginformation regarding all position and attitude states. This can enablethe navigating object to produce a more accurate navigation stateestimate.

For example, the self-describing fiducial (1300) may include informationthat communicates its identifier/location. This may take a variety offorms including the multidimensional barcode (1302) shown on the outersurface of the sphere or any appropriate form. In one embodiment, theinformation communicated could be dependent on the direction the selfdescribing fiducial was imaged from. This could allow the navigatingobject observing the self-describing fiducial to determine what angle itis approaching the self-describing fiducial from. Additionally, theinformation in the multidimensional barcode (1302) may communicatecharacteristics of additionally features present, how tointerpret/identify the features, information communicated by thefeatures, applicability of the information, etc. In one embodiment, thesize of the sphere may be communicated or predetermined. Thus, theapparent size of the sphere on the navigating object's sensor can givean estimate of the distance between the self describing fiducial and thenavigating object.

In this example, the self-describing fiducial (1300) also includes analignment or registration mark (1308) that can be used for a variety ofpurposes, including communicating a range-to-target, perspective basedinformation, and/or work in combination with other components of theself describing fiducial. The self-describing fiducial (1300) in thisexample also includes three orthogonal arrows (1304) that are uniquelyidentifiable by bands (1306). This may allow the navigating object todirectly observe its look vector from a single observation, theorientation of the fiducial, and its own orientation with respect to thefiducial.

In general, self-describing fiducials may communicate more than theirlocation. For example, they may indicate their orientation with respectto one or more coordinate systems or objects. They may communicatedirectionality/identification of a path, location of obstacles orhazards, traffic conditions, a landing point, a charging station and/orenvironmental conditions. They may indicate actions that the navigatingobject should take (turn left, right, do not enter, warning, caution,yield, stop, etc.).

In many instances, different navigating objects may need to followdifferent paths. In one embodiment, a self-describing fiducial couldcommunicate information about what type of navigating object shouldfollow a certain path. For example, a self-describing fiducial couldcommunicate that heavier navigating objects should travel at a higheraltitude, but that lighter navigating objects should travel at a loweraltitude. In another example, a self-describing fiducial couldcommunicate that a certain type of navigating object should turn left,but that a different type of navigating object should turn right. Inanother example, a self-describing fiducial in windy conditions couldcommunicate that lighter navigating objects need to land, andcommunicate that heavier navigating objects could proceed.

In another embodiment, a self-describing fiducial could be encoded sothat only specific navigating objects equipped with the decodinginformation could receive information from the self-describing fiducial.This could help prevent fraudulent use of self-describing fiducials tomisdirect or misinform navigating objects.

In another example, navigating objects could be configured tocommunicate only with self-describing fiducials that contain a uniqueidentifier. For example, navigating objects going to a certaindestination would only communicate with self-describing fiducials thatlead to that destination, while navigating objects going to a differentdestination would communicate with different self-describing fiducialsthat lead to a different destination.

As discussed above, one example of a system of self-describing fiducialsprovides positional information to nearby navigating objects. Onepotential application of such a system is to support the navigation ofobjects such as unmanned aerial vehicles (UAV) in metropolitan areas,for the purposes of package delivery, air taxis, etc. An importantfactor in the flight of an UAV is the state of the surrounding air flow.The air flow through metropolitan areas exhibits stratification and highlevels of vorticity and turbulence. For example, due to their relativelysmall mass and inertia, Group 1 (0-20 lbs) and Group 2 (21-55 lbs) UAVsare particularly susceptible to unexpected or adverse flow fields. OtherUAVs or other navigating objects can also be influenced by unknown flowfields to varying degrees. As aerial highways become more congested, thelack of knowledge of these dynamic flow fields will result in greateruncertainty in the flight path of navigating objects and an associatedincreased risk of collision amongst navigating objects. In addition tosafety concerns, adverse (or opposing) flow fields increase drag on theUAV or navigating object, resulting in shorter flight times and lessefficient package or passenger delivery.

FIG. 14 shows an urban landscape (1400) with a navigating object (1412)traveling through a simplified flow field in an area that is dominatedby a number of buildings (1402) with spaces between them. While thedesired path of the navigating object (1412) is centered betweenbuildings, the mean path (1406) is offset due to aerodynamic forcescaused by the adverse air flow from right to left (1408, 1410). Theuncertainty envelope/position dispersion (1404) of the navigatingobject's path also increases due to the variability of the flow field.

Knowledge of the flow field enables more intelligent path planning bycompensating for its effect, both from a safety as well as an efficiencyperspective. For example, efficiencies may be gained in energy and time.These efficiencies may include an increase in the number of packagesdelivered, shorter flight times, longer endurance, less energy expendedin a given flight path, more optimal flight path planning, loweruncertainty in position dispersion, better collision avoidance, abilityto fly in tighter flight formations, etc. This can be achieved byexploiting knowledge of the flow field for the routing of a vehicle orvehicles, avoiding periods of heavy adverse flow, or routing vehicleswith low power levels/bulky payloads through areas with less adverseflow.

Other illustrative embodiments may include a path planner that exploitsknowledge of the flow field and associated uncertainty to safely directtraffic of navigating objects with sufficient space around each objectto maintain the risk of collisions below a specified threshold. The pathplanner may be part of a navigating object(s), a self-describingfiducial(s) or a centralized entity. For example, an air traffic controlentity may use flow field knowledge to maintain safe spacing betweennavigating objects, by compensating for the effects of the flow.

FIG. 15 shows a more complex flow field (1504) that passes through anumber of buildings (1502). In this example, there are multiplenavigating objects (i.e. 1506, 1516) traveling along an aerial pathbetween buildings (1502). There are a number of self-describingfiducials (1510, 1512, 1514) along the path. In this example, thewind/flow field (1504) is blowing from the right to left. The buildings(1502) and other obstructions influence the flow field (1504) andproduce spatial and temporal variations in the speed and direction ofthe flow. For example, when the flow is constrained to pass betweenbuildings, the flow may be accelerated. As the flow hits a buildingthere may be stagnation points near the building on the windward side.There may also be turbulence or vortices on the lower pressure surfaces(i.e. the lee side of buildings). This results in a complex flow fieldthat varies spatially and over time. For example, in areas withturbulence, the wind can quickly change directions and intensities.These changes in flow direction and speed can disturb/disrupt navigatingobject travel.

In FIG. 15, there are five navigating objects (including 1506, 1516)passing through the area (moving from the bottom to the top of the page)through the opening between buildings. The goal is for these navigatingobjects to efficiently travel along a designated path(s) with adequateclearance between themselves and their surroundings. They need to avoidstationary objects (such as buildings and poles), quasi stationaryobjects (such as trees and swinging power lines), and moving objects(such as other navigating objects, humans, birds, etc.). However, due tochanging flow conditions, there may be a relatively large uncertainty inthe conditions and path that the navigating objects will take. Eachnavigating object in FIG. 15 shows a dashed-dotted shape surrounding itthat describes the uncertainty. This dash-dotted shape grows larger whenthere are higher flow variations. For example, a first navigating object(1506) may be relatively lightweight and travelling in an area with highspeed and/or more variable flows. This results in a greater uncertaintyin its ability to control its position as indicated by the relativelylarge uncertainty envelope (1508). When the uncertainty envelope of anavigation object is far from a building or another navigating object,the risk of collision is low. The more the uncertainty envelopes overlapwith adjacent objects/uncertainty envelopes, the higher the chance ofcollision.

For example, the uncertainty envelope (1508) impinges on both a building(1502) and the uncertainty envelope of a neighboring a navigating object(1518). This means that there is a risk that the navigating object(1506) may collide with either (or both) the building and the navigatingobject ahead of it. This situation could have been avoided if thenavigating object (1506) had positioned itself better in the spacebefore entering the flow (i.e. a greater following distance behind thenavigating object (1518) ahead of it and/or moved farther to the right,slowed down, etc.) or had taken an alternative route (i.e. changealtitude, follow a different path with less concentrated flow, etc.).However, this presupposes that the navigating object (1506) knew aboutthe real time/predicted flow characteristics in its vicinity in advance,and appropriately adjusted its trajectory to compensate.

In contrast, the second navigating object (1516) has a small uncertaintyoval. This may be for any number of reasons, including but not limitedto the following: the navigating object (1516) may be heavier, morepowerful, and/or better aerodynamics and consequently is less impactedby external flows, the navigating object (1516) may be in a slower areaof the flow (i.e. in the center of an eddy, or lower to the ground), orsome other reason.

The navigating objects may receive advanced notice of the flowcharacteristics from the self-describing fiducials (1510, 1512, 1514).For example, in addition to communicating their location, theself-describing fiducials may communicate additional information such aslocal characteristics of the flow. FIGS. 16-20 show a number ofillustrative examples of self-describing fiducials that could measure orobtain information about flow fields in their proximity. Returning toFIG. 15, the location of the self-describing fiducials (1510, 1512,1514) may be selected to make the relevant measurements of the flowfield (1504) and to be visible to vehicles or navigating objects thatwill encounter the flows.

In the example shown in FIG. 15, the self-describing fiducial (1514) mayhave been visible to the navigating object (1506) prior to thenavigating object encountering the high velocity cross wind between thebuildings (1502) that is threatening to blow the navigating object(1506) into the building to the left. The self-describing fiducial(1514) could make a wind measurement and communicate to the navigatingobject (1506) the direction and velocity of the flow. In some examples,the self-describing fiducial may only measure the flow in its locationand the flow field in the intended path of the navigating object mayneed to be calculated/modelled/extrapolated. This can be relativelystraightforward because the calculation/model could be calibrated eachtime a navigating object passes through the area. In other examples, theself-describing fiducial may directly measure the flow velocity alongthe navigating object's intended path (see e.g. the ultrasonic/radarwind sensor (1802) shown in FIG. 18), or the self-describing fiducialmay receive (additional) information from external sources (such aswireless transmissions from other navigating objects (e.g. FIG. 20) orfrom other weather services).

However, even if the self-describing fiducial (1514) was not visible tothe navigating object (1506), the information from the otherself-describing fiducials (1510, 1512) along with models/previousexperience could be used to predict the flow in the proximity of theself-describing fiducial (1514). For example, only one of theself-describing fiducials (1510, 1512, 1514) may include an airspeedsensor, while the other fiducials may include an orientation vector, awind direction sensor, or other sensor/feature.

FIGS. 16-20 illustrate embodiments of self-describing fiducialsaugmented with airspeed information such as the velocity, direction, andtemporal variations of the flow. This information can serve a number ofpurposes, including building/using a model to estimate flows along apath or in the vicinity of a vehicle. The fidelity of the model variesby application and may include models based on laminar potential flow orcomputational fluid dynamics. The flow model and/or path planner couldbe a centralized node in a network with greater computational powerand/or connectivity than other nodes, or the flow model/path planningcould be done in a more distributed manner, with each node focusing onthe computations that are more relevant to its locale and/or predictedpath. These calculations of the flow field could include models ofcities/buildings and weather models to create an estimate of the flowfield. Point measurements made by the self-describing fiducials can beused to create/improve the flow field model/calculation and/or can beused to improve an existing flow field estimate.

Given the envisioned ubiquity of the self-describing fiducial system,knowledge of the flow field can be aided by augmenting eachself-describing fiducial with flow field sensors. Point measurements ofthe flow serve as reference points in a large-scale flow field model.Given an aerodynamic model of the navigating object and knowledge of itsnavigation and control algorithms, the mean path and the associateduncertainty envelope of the navigating object can be predicted prior tothe actual flight, as illustrated in FIGS. 14 and 15. Energy expenditurealong the path due to drag, lift, etc. can also be predicted. As is thecase in flow field modeling, methods for predicting the uncertaintyenvelope and energy expenditure vary in complexity, ranging fromefficient closed-loop linear covariance analysis to high-fidelity MonteCarlo analysis or other suitable computational algorithms.

To ensure safe travel of large numbers of navigating objects, theself-describing fiducial system can direct the traffic of navigatingobjects in such a way that separation distances between objects meet aspecified probability of collision. It is important to note that therequired separation depends on the size of the uncertainty envelope,which in turn depends on the flow field, aerodynamics, and thenavigation estimation and guidance algorithms of each navigating object.

The self-describing fiducial may take a wide variety of forms, includinga self-describing fiducial that communicates a wind vector, an energycost for navigating the region, a suggested path modification, or someother data. In one embodiment, the self-describing fiducial communicatesthe parameters of the flow field along the navigating object's intendedflight path. With this information, a navigating object can prepare sothat the flow field doesn't negatively affect its performance. Forexample, the navigating object can make calculations using a model ofits own performance to modify its flight path/parameters. The navigatingobject may alter its flight path to pre-position itself so a cross winddoesn't undesirably cause the navigating object to leave the flightcorridor. Alternatively, the navigating object may adjust its height sothat it is traveling at a lower altitude with calmer winds.

In another example, the self-describing fiducial may communicate a meanflow vector and a standard deviation of the flow. Additionally oralternatively, the self-describing fiducial may communicate a gradientor a derivative of the flow and/or a wind vector flow field. In oneembodiment, the intended path of the navigating object may be known bythe self-describing fiducials. The self-describing fiducial may belocated near one or more predetermined flight corridors that thenavigating object is traveling along and consequently may understand thepredicted flight path of the navigating object. The navigating objectmay calculate a flight path and may communicate that flight path to theself-describing fiducial/system.

In cases where the intended/predicted flight path is known, the flowfield may be calculated along the flight path. For example, theself-describing fiducial system may provide the mean flow at pointsalong the intended path. Additionally, one or more elements in thesystem may calculate a wind vector flow field. For example, aself-describing fiducial may calculate the flow field value at gridpoints around its location for 100 meters and communicate thisinformation to the navigating object. The navigating object may thenapply this data to improve its performance as described above. The gridmay be two dimensional or three dimensional and may contain scalarvalues at points in the grid or may include vectorized information. Asdiscussed below, where there is relatively dense traffic of navigatingobjects, the navigating objects may act as mobile sensors that detectwind and other conditions and may communicate this information to one ormore nodes in the system. In some examples, the flow field modeling andother calculations could be performed by one or more centralized nodes.These nodes could distribute relevant information from the models tonodes that could utilize the information. For example, a flow field ispredicted based on a model of the city geometry, weather predictions,measurements from self-describing fiducials and other parameters may becombined with navigating object flight path and other vehicle trafficpatterns to generate optimized system performance. In one embodiment,knowledge of the flow field, aerodynamics, and navigation estimation andguidance algorithms may be used to select paths which minimize theenergy usage of navigating objects with limited resources for propulsionor large payloads.

FIGS. 16, 17, 18 and 19 illustrate various sensors that could be used tosense flow fields in the vicinity of the self-describing fiducial. Thesensors could be various shapes, sizes and use a variety of techniquesto measure/sense relevant parameters. FIGS. 16 and 17 showself-describing fiducials (1600, 1700) that include an airspeed sensor(1602, 1702/1704) with one or more sensitive axes that could be used tosense flow fields in the vicinity of the self-describing fiducial (1600,1700). FIG. 16 shows a self-describing fiducial (1600) that includes amount and a pole (1606) with a directional element (1608), aself-describing fiducial (1604), and a cup anemometer (1602) whichmeasures wind speed in the horizontal plane. This sensor produces apoint measurement of airspeed which serves to improve the accuracy of amodel of the flow field. FIG. 17 shows augmentation of theself-describing fiducial (1700) with an aerodynamic lifting surface(1702) mounted to a load cell (1704). As the air passes over the liftingsurface (1702), it produces forces which are measured by the load cell(1704). Lifting line theory (or other appropriate theory/computationalmethod) is then used to relate the measured forces to vorticity of theflow, which in turn is used to predict the aerodynamic forces acting onnearby navigating objects.

FIG. 18 illustrates augmentation of the self-describing fiducial (1800)with a doppler lidar (1802) wind measurement system, which providesmeasurements of wind velocity throughout the flow field area or volume(1810). In FIG. 19, the self-describing fiducial (1900) is augmentedwith a three-dimensional ultrasonic anemometer (1902), which measures athree-dimensional wind vector representing the flow field (1910). Theabove examples include examples of mounting hardware (1606, 1710, 1806,1906), directionality features (1608, 1708, 1808), and elements designedto communicate location information (1604, 1706, 1804, 1904). In thisexample, the element designed to communicate the location of theself-describing fiducial is illustrated as a disk, but could have anynumber of shapes or embodiments, including those shown above in FIGS.3A, 3B, 3C, 3D, 7, 8, 9, 10, 11, 12, 13. The element designed tocommunicate location may be of any appropriate size and have any of anumber of features.

These are only a few examples of sensors and self-describing fiducialsthat could be used to measure flow fields throughout an aerial highway,further improving the fidelity of the large-scale flow field modeland/or efficiency of navigating objects while traveling. A variety ofother sensors, systems, architectures, computational implementations,network configurations, etc. could be used. For example, as illustratedin FIG. 20, the navigating objects (2002) themselves may be used totransmit estimates (2006) of the flow field (2012) as sensed via onboardsensors (2004) (such as pitot tube and static sensors) or derived fromother parameters such as energy expended, flight corrections,accelerations experienced, models of the flightparameters/characteristics of the navigating object (2002), and/or otherappropriate parameters/models. For example, the navigating object'sairspeed combined with the ground speed estimates from the object'snavigation system can provide an estimate of wind speed at the locationof the navigating object (2002). Alternatively, the wind speed can beestimated by combining information in an aerodynamic model of thenavigating object (2002), propeller thrust, and ground speed estimatesfrom the navigation system. The wind speed information is transmitted tothe self-describing fiducial system (2000) to improve the model of theoverall flow field. As discussed above the self-describing fiducialsystem (2000) may include a variety of elements, including a mount(2014), a directionality component (2008), a feature that communicatesits location (2010), etc. The self-describing fiducial system (2000) maybe in communication with a variety of different components and networks.

The supplementary information may be communicated in any appropriateway, including one-way communication from a self-describing fiducial tonavigating objects, through two way communication between one or moreself-describing fiducials and navigating objects or through networkedcommunication that distributes information between the nodes, where thenodes may include self-describing fiducials, navigating objects, othernavigating objects, satellites, stationary objects/infrastructure, etc.The supplementary information may be communicated in a variety of ways,including but not limited to communicating optically, through any of anumber of wireless protocols, general broadcast, or other method.

FIG. 21 shows a method (2100) of navigation using self-describingfiducials. In one embodiment, a navigating object can observe aself-describing fiducial in its vicinity (step 2105). Theself-describing fiducial may then optically communicate itsposition/location to the navigating object (step 2110). In addition, theself-describing fiducial could communicate supplementary information tothe navigating object (step 2115). The navigating object could thenprocess the location of the self-describing fiducial and/or any othersupplementary information (step 2120) and make adjustments to itsnavigation states and/or guidance parameters (step 2125).

FIG. 22 shows a method (2200) of navigation using self-describingfiducials in which the self-describing fiducial could communicateinformation about the applicability of the supplementary information,and/or additional characteristics of the supplementary information. Inthis example, a navigating object could observe a self-describingfiducial in its vicinity (step 2205). The self-describing fiducial couldthen communicate its location and/or supplementary information to thenavigating object (steps 2210, 2215). The self-describing fiducial couldalso communicate additional characteristics about the supplementaryinformation to the navigating object (step 2220), such as the format orunits of the information. The self-describing fiducial could alsocommunicate applicability information to the navigating object (step2225), which could inform the navigating object which objects thesupplementary information applies to. The navigating object could thenprocess the applicability information to determine if the supplementaryinformation is applicable to the navigating object (step 2230). If thesupplementary information is applicable to the navigating object (step2235), the navigating object could process the location and/orsupplementary information (step 2240), and could make changes to itsnavigation state and guidance parameters (step 2245). For example, anavigating object could receive information from a self-describingfiducial that high winds are ahead, and applicability information thatthe high winds will most likely affect lightweight or bulky objects. Thenavigating object could determine (step 2235) that, because of itsshape, this information is not applicable to it, and therefore notadjust its guidance parameters (step 2250).

FIG. 23 shows an additional method (2300) for navigation usingself-describing fiducials. In one example, the navigating objectobserves the self-describing fiducial (step 2305) and theself-describing fiducial communications its location to the navigatingobject (step 2310). The self-describing fiducial may also communicate:supplementary information to the navigating object (step 2315),communicate the characteristics of the supplementary information to thenavigating object (step 2320), and communicate applicability information(step 2325). The self-describing fiducial may also communicatevalidation information to a navigating object confirming that theself-describing fiducial's location and supplementary information can betrusted by the navigating object (step 2330). For example, thevalidation information could confirm that the location and supplementaryinformation are from a trusted source, are up to date, and/or have notbeen fraudulently changed. The navigating object processes theapplicability information (step 2335) and using outcome of thisprocessing determines if the received information is applicable to it(step 2340).

If the information is applicable (“Yes” step 2340), the navigatingobject processes validation information (step 2345) and determines ifthe received information is valid (step 2350). If this determination(“No” step 2350) or the previous determination (“No” step 2340) thenavigating object does not process at least one of the location andsupplementary information from the self describing fiducial (step 2380).

If the received information is both applicable and valid, the navigatingobject processes at least one of the location and supplementaryinformation from the self-describing fiducial (step 2355). Based on thevalidation information, the navigating object could determine if itshould adjust its navigation state and guidance parameters (step 2375).For example, a navigating object could receive validation communicationfrom a self-describing fiducial that it was created by a certainorganization, and supplementary information to turn left. If thenavigating object is associated with that organization, it wouldincorporate the supplementary information and turn left. However, if thenavigating object was not associated with the organization, it would notprocess the supplementary information and would continue its originalcourse.

In another embodiment, based on information known by a navigatingobject, the navigating object could evaluate the reliability ofinformation received from a self-describing fiducial (step 2360). Thenavigating object could then assign a reliability score to theinformation from the self-describing fiducial (step 2365), weigh theinformation based on the reliability score (step 2370) and adjust itsnavigation state and guidance parameters based on the weighting of theinformation (step 2375). For example, if a navigating object receivesinformation from a self-describing fiducial that high winds are ahead,but this information contradicts information from instruments onboardthe navigating object or information from other self-describingfiducials in the area, the navigating object could assign a lowreliability score to the information and only adjust its navigationstate and guidance parameters slightly. However, if the information fromthe self-describing fiducial is consistent with the onboard instruments,the navigating object could assign a high reliability score to theinformation and incorporate it by making large adjustments to itsnavigation state and guidance parameters.

Additionally or alternatively, a navigating object could calculate theconfidence level of the information that it receives from aself-describing fiducial. For example, if a self-describing fiducialcommunicates a location that is extremely different from what isexpected or is different from what other self-describing fiducials inthe area are communicating, a navigating object could place lowconfidence in the information and disregard it. In another example, aself-describing fiducial that has been accurately surveyed communicatesits position with an associated low position uncertainty. The navigationobject receives this navigation aiding information and reliabilityscore, and appropriately adjusts its navigation state given thecommunicated reliability of the navigation-aiding information.

Thus, in one example of principles described herein, a self-describingfiducial may include a communication element that is configured tooptically communicate navigation state estimation aiding information toone or more navigating objects in the vicinity of the self-describingfiducial. The navigation-aiding information may include a geographicposition of the self-describing fiducial with respect to one or morecoordinate systems. The communication element may be further configuredto communicate supplementary information describing a spatialrelationship between the self-describing fiducial and the surroundingenvironment.

The supplementary information may include at least one of additionalnavigation-aiding information and guidance-aiding information. Thegeographic position may include a three dimensional position. Thesupplementary information may include an external directionality of afeature of the self-describing fiducial. For example, the supplementaryinformation may include attitude of the self-describing fiducial withrespect to one or more coordinate systems, wherein the attitudeinformation comprises at least one of: cardinal directions, compassheading, true north, magnetic north, path direction, zenithdirectionality, nadir directionality and a gravitational orientation.The self-describing fiducial may be enhanced with an associatedobservable attitude describing feature comprising at least one of: anoriented matrix barcode, an arrow, a triangle, a row of lights, a matrixbarcode wrapped on a cylinder, a matrix barcode on a sphere or otherappropriate feature or geometry that conveys directionality or attitude.

The supplementary information may also include at least one ofperspective-based information and range to target. In one example, thesupplementary information may include at least one of a pointmeasurement of wind speed in one or more directions, a volumetricmeasurement of wind speed in one or more directions, the force generatedby wind on a feature of the self describing fiducial, an in situ windmeasurement produced by a navigating object in the vicinity of the selfdescribing fiducial. The supplementary information may include a spatialguidance command, such as indicating a turn, an altitude, a caution toavoid an obstacle, a speed vector, or other spatial instructions orwarnings.

The communication element may include a first optical communicationcomponent comprising an optically observable description of the positionof the self-describing fiducial and a second communication componentconfigured to communicate the supplementary information. Thesupplementary information may include any of the principles or examplesdescribed above, including a spatial relationship between theself-describing fiducial and the surrounding environment. The firstcommunication element may be further configured to communicate: theexistence of the second communication component and/or supplementaryinformation, at least one characteristic of the second communicationcomponent and/or supplementary information, the applicability of thesecond communication element and/or supplementary information. The firstcommunication component may include one or more of: a printed matrixbarcode, a projected printed matrix barcode, a barcode, an opticalbeacon comprising a modulated LED, a modulated traffic light, anenhanced billboard, a light on a navigating object or other appropriateelement configured to communicate information. The second communicatingcomponent may include at least one of a matrix barcode, at least onemodulated LED, a radio frequency communication device, a lasercommunication device, and other appropriate element(s) configured tocommunicate information.

Additionally or alternatively, a self-describing fiducial may include acommunication element configured to communicate air flow information toone or more navigating objects, the air flow information comprising airflows interacting with local obstructions at or near ground level. Forexample, the term “at ground level” may include altitudes where dronesnavigate through urban canyons, or may include any portion of airflowthat disturbed by objects on the ground, or may include altitudes wheredrones typically navigate to deliver objects (for example, below 500 ftelevation above ground level) or the term “at ground level” may describethe portion of the airflow that the airflow information is relevant to.The communication element may communicate the air flow information tothe one or more navigating objects in the vicinity of theself-describing fiducial. For example, air flows may interact with localobstructions at or near ground level which may include buildings, trees,signs, large vehicles, towers, bridges, sculptures, local terrainfeatures and other ground based objects and features.

In one example, the self-describing fiducial may include an air flowmeasurement device, wherein the air flow measurement device produces atleast one of: a point measurement of wind speed in one or moredirections, a volumetric measurement of wind speed in one or moredirections, the force generated by wind on a feature of theself-describing fiducial an in situ wind measurement produced by anavigating object in the vicinity of the self-describing fiducial.Measurements produced by the air flow measurement device may be combinedwith additional information to produce an enhanced air flow model. Theadditional information may include at least one of: other air flowmeasurements, local air flow model, local weather forecast, localgeometric models, information from other self-describing fiducials, andother available information or models.

The self describing fiducial may include a second communication elementthat is configured to communicate a position of the self-describingfiducial with respect to one or more spatial coordinate systems to oneor more navigating objects.

In one embodiment a method includes observing, by a navigating object, aself-describing fiducial in the vicinity of the navigating object andoptically communicating, by a communication element of theself-describing fiducial to the navigating object, a position of theself-describing fiducial. The method may further include communicating,by the self-describing fiducial, supplementary information to thenavigating object and processing, by the navigating object, at least oneof: the position of the self-describing fiducial and the supplementaryinformation. The method may further include making, by the navigatingobject, an adjustment to at least one of: navigation states of thenavigating object and guidance parameters of the navigating object. Asdiscussed herein the guidance parameters may include a variety ofgeometric instructions, including but not limited to turning radius,altitude, speed, next waypoints, locations of additional self-describingfiducials or obstructions, locations of other navigating objects, etc.

The method may include communicating, by the communication element,characteristics of the supplementary information. The method may alsoinclude communicating applicability information of the self-describingfiducial to the navigating object and processing, by the navigatingobject, the applicability information to determine if supplementaryinformation is applicable to the navigating object. Based on this orother determinations, changes can be made to at least one of: navigationstates and guidance parameters of the navigating object.

The method may further include communicating, by the self-describingfiducial to the navigating object, validation information andvalidating, by the navigating object, the self-describing fiducial; andbased on the validating, incorporating the position of theself-describing fiducial and supplementary information into at least oneof: navigation states and guidance parameters of the navigating object.

The method may also include evaluating, based on information known bythe navigating object, the position reported by the self-describingfiducial and the supplementary information reported by theself-describing fiducial and assigning a reliability score to theposition reported by the self-describing fiducial and the supplementaryinformation reported by the self-describing fiducial based on theevaluating and weight weighting the position reported by theself-describing fiducial and the supplementary information reported bythe self-describing fiducial based on the reliability score. This caninclude applying, to navigation states and guidance parameters of thenavigating object, the position reported by the self-describing fiducialand the supplementary information reported by the self-describingfiducial based on the weighting. The methods described herein are onlyillustrative and the steps of the method may be reordered, additionalsteps may be added, and steps may be removed or replaced.

While the foregoing written description of the invention enables one ofordinary skill to make and use what is considered presently to be thebest mode thereof, those of ordinary skill will understand andappreciate the existence of variations, combinations, and equivalents ofthe specific embodiment, method, and examples herein. The inventionshould therefore not be limited by the above described embodiment,method, and examples, but by all embodiments and methods within thescope and spirit of the invention.

What is claimed:
 1. A self-describing fiducial comprising: acommunication element that is configured to optically communicatenavigation state estimation aiding information to one or more navigatingobjects in the vicinity of the self-describing fiducial for stateestimation, wherein the navigation-aiding information comprises ageographic position of the self-describing fiducial with respect to oneor more coordinate systems; and the communication element is furtherconfigured to communicate supplementary information describing a spatialrelationship between the self-describing fiducial and the surroundingenvironment for state estimation and guidance, wherein the supplementaryinformation comprises at least one of: additional navigation-aidinginformation and guidance-aiding information.
 2. The fiducial of claim 1,wherein: the communication element comprises at least one of a lightreflecting surface or a light emitting surface to optically communicatenavigation-aiding information to at least one navigating object; andwherein the self-describing fiducial is configured to be visible to atleast one navigating object on a path and wherein the navigating objectis configured to optically detect the self-describing fiducial, receivethe navigation-aiding information, determine least one of its position,velocity or orientation with respect to the self-describing fiducial,wherein the navigating object moves along an altered path based on thenavigation-aiding information.
 3. The fiducial of claim 1, wherein theposition comprises a three dimensional position and the supplementaryinformation comprises an external directionality of a feature of theself-describing fiducial.
 4. The fiducial of claim 1, wherein thesupplementary information comprises attitude of the self-describingfiducial with respect to one or more coordinate systems, wherein theattitude information comprises at least one of: cardinal directions,compass heading, true north, magnetic north, path direction, zenithdirectionality, nadir directionality and a gravitational orientation. 5.The fiducial of claim 4, wherein the self-describing fiducial isenhanced with an associated observable attitude describing featurecomprising at least one of: an oriented matrix barcode, an arrow, atriangle, a row of lights, a matrix barcode wrapped on a cylinder, and amatrix barcode on a sphere.
 6. The fiducial of claim 1, wherein thesupplementary information comprises at least one of: perspective-basedinformation and range to target.
 7. The fiducial of claim 1, wherein thesupplementary information comprises at least one of: a point measurementof wind speed in one or more directions, a volumetric measurement ofwind speed in one or more directions, a force generated by wind on afeature of the self-describing fiducial, an in situ wind measurementproduced by a navigating object in the vicinity of the self-describingfiducial.
 8. The fiducial of claim 1, wherein the communication elementcomprises a first optical communication component comprising anoptically observable description of the position of the self-describingfiducial and a second communication component configured to communicatethe supplementary information.
 9. The fiducial of claim 8, wherein thefirst optical communication component is further configured tocommunicate existence of the second communication component.
 10. Thefiducial of claim 8, wherein the first optical communication componentcommunicates at least one characteristic of the second communicationcomponent.
 11. A self-describing fiducial comprising: a communicationelement configured to communicate air flow information to one or morenavigating objects, the air flow information comprising air flowsinteracting with local obstructions at ground level, wherein thecommunication element communicates the air flow information to the oneor more navigating objects in the vicinity of the self-describingfiducial, altering a trajectory of the one or more navigating objectsbased on the air flow information.
 12. The fiducial of claim 11, whereinthe self-describing fiducial comprises an air flow measurement device,wherein the air flow measurement device produces at least one of: apoint measurement of wind speed in one or more directions, a volumetricmeasurement of wind speed in one or more directions, the force generatedby wind on a feature of the self-describing fiducial, an in situ windmeasurement produced by the one or more navigating objects in thevicinity of the self-describing fiducial.
 13. The fiducial of claim 12,wherein measurements produced by the air flow measurement device arecombined with additional information to produce an enhanced air flowmodel, wherein the additional information comprises at least one of:other air flow measurements, local air flow model, local weatherforecast, local geometric models, and information from otherself-describing fiducials.
 14. The fiducial of claim 11, furthercomprising a second communication element that is configured tocommunicate a position of the self-describing fiducial with respect toone or more spatial coordinate systems to one or more navigatingobjects.
 15. The fiducial of claim 11, wherein the local obstructions atground level comprise one or more of buildings, trees, signs, largevehicles, towers, bridges, sculptures, and local terrain features.
 16. Amethod comprising: observing, by a navigating object, a self-describingfiducial in the vicinity of the navigating object; opticallycommunicating, by a communication element of the self-describingfiducial to the navigating object, a position of the self-describingfiducial; communicating, by the self-describing fiducial, supplementaryinformation to the navigating object; processing, by the navigatingobject, at least one of: the position of the self-describing fiducialand the supplementary information; making, by the navigating object, anadjustment to at least one of: navigation states of the navigatingobject and guidance parameters of the navigating object; altering atrajectory of the navigating object based on the adjustment.
 17. Themethod of claim 16, further comprising communicating, by thecommunication element, characteristics of the supplementary information.18. The method of claim 16, further comprising: communicatingapplicability information of the self-describing fiducial to thenavigating object; processing, by the navigating object, theapplicability information to determine if supplementary information isapplicable to the navigating object; based on the determination, makingchanges to at least one of: the navigation parameters and guidanceparameters of the navigating object.
 19. The method of claim 16, furthercomprising: communicating, by the self-describing fiducial to thenavigating object, validation information; validating, by the navigatingobject, the self-describing fiducial; and based on the validating,incorporating the position of the self-describing fiducial andsupplementary information into at least one of: navigation states andguidance parameters of the navigating object.
 20. The method of claim16, further comprising: evaluating, based on information known by thenavigating object, the position reported by the self-describing fiducialand the supplementary information reported by the self-describingfiducial; assigning a reliability score to the position reported by theself-describing fiducial and the supplementary information reported bythe self-describing fiducial based on the evaluating; weighting theposition reported by the self-describing fiducial and the supplementaryinformation reported by the self-describing fiducial based on thereliability score; applying, based on the weighting, the positionreported by the self-describing fiducial and the supplementaryinformation reported by the self-describing fiducial to at least one of:navigation states and guidance parameters of the navigating object.